Comply
Financial Services
SeniorAIDataEngineer
“Senior AI Data Engineer at Comply. Skills: Knowledge Graphs, Vector databases, LLM-powered pipelines, Data platform engineering. Implement JSON-LD-based semantic models. Build knowledge graph structures”
What You'll Achieve.
ensure AI-ready data products are reliable; ensure AI-ready data products are performant; ensure AI-ready data products are adopted in practice
Industry & Context.
resolving cross-domain data integration challenges
must be authorized to work for any employer in the United Kingdom, unable to sponsor or take over sponsorship of an employment Visa
What They're Looking For.
Must Have
hands-on experience in data engineering, semantic or AI data infrastructure, building and operating knowledge graphs or graph databases, vector databases and embedding pipelines, implementing RAG architectures or LLM-integrated data pipelines, semantic web standards — JSON-LD, RDF, OWL, or SKOS, Python skills, data pipeline frameworks, cloud-native data platforms (AWS, Azure, or GCP), DataOps tooling, data reliability, data observability platforms, authorized to work for any employer in the United Kingdom
Nice to Have
Experience building and operating knowledge graphs or graph databases (e.g. Jena Fuseki, Neo4j, Amazon Neptune, or equivalent), Experience with vector databases and embedding pipelines (e.g. Pinecone, Weaviate, Qdrant, pgvector), Exposure to domain-driven design (DDD), bounded contexts, working directly with ontologists or knowledge engineers, data contracts, data product frameworks, Background in financial services, RegTech, compliance data
What You'll Do.
Implement JSON-LD-based semantic models
Build knowledge graph structures
Develop graph database schemas
Design embedding pipelines
Build vector database infrastructure
Implement RAG architectures
Build reliable data pipelines
Apply DataOps practices
Embed semantic models into APIs
Ensure semantic layer scales
How You'll Work.
Team & Collaboration
Collaborate with application teams; Collaborate with data engineering teams; Work with Data Engineers; Work with Backend Engineers; Partner with Ontologist; Support consuming application teams
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